Build and update RAG system with Google Drive, Qdrant, and Gemini Chat
n8n24 modulesv1.0This workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It enables full or incremental updates to documents in the Qdrant vector database and integrates with a chatbot using Google Gemini for question answering. Here is a clear and professional description in English of the n8n workflow “Create a RAG with Qdrant and update single files”, including its benefits: ---
At a glance
Build and update RAG system with Google Drive, Qdrant, and Gemini Chat is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects OpenAI, Google Drive, LinkedIn, Gemini. It's free to download. Follow the 5-step import below to go live in minutes.
- Platform
- n8n
- Connects
- OpenAI, Google Drive, LinkedIn, Gemini
- Modules
- 24
- Price
- Free
- Version
- v1.0
About this workflow
This workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It enables full or incremental updates to documents in the Qdrant vector database and integrates with a chatbot using Google Gemini for question answering. Here is a clear and professional description in English of the n8n workflow “Create a RAG with Qdrant and update single files”, including its benefits: --- Benefits Efficient RAG Setup Seamlessly integrates OpenAI, Qdrant, and Google Drive to create a scalable RAG pipeline. Single File Update You can replace the vector representation of a single file without reprocessing the entire collection—ideal for maintaining document freshness. Flexible File Source Works with Google Drive, allowing document management and updates from a familiar interface. --- How It Works This workflow is designed to create a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as a document source. It consists of four main phases: - Collection Setup: - Creates or clears a Qdrant collection to store vectorized documents. - Configures the collection with cosine distance metrics and other parameters. - Document Processing: - Retrieves files from a specified Google Drive folder. - Downloads and processes each file (text extraction, chunking, and embedding using OpenAI). - Stores the embeddings in Qdrant for vector search. - Single-File Update: - Allows updating or deleting a specific file in the Qdrant collection by referencing its Google Drive ID. - Re-embeds the file and updates the vector store. - RAG Querying: - Uses a chat trigger to receive user questions. - Retrieves relevant documents from Qdrant using vector similarity. - Generates answers using Google Gemini as the language model. --- Set Up Steps 1. Configure Qdrant: - Replace QDRANTURL and COLLECTION in the "Create collection" and "Clear collection" HTTP nodes. - Ensure Qdrant API credentials are correctly set in the credentials section. 2. Google Drive Integration: - Specify the Google Drive folder ID in the "Get files" node. - Ensure Google Drive OAuth credentials are configured. 3. OpenAI and Gemini Keys: - Add OpenAI API credentials for embeddings (used in "Embeddings OpenAI" nodes). - Configure Google Gemini credentials for the chat model. 4. Single-File Update: - Set the fileid in the "Edit Fields3" node to target a specific Google Drive file for updates. 5. Testing: - Trigger the workflow manually to populate the Qdrant collection. - Use the chat interface to test RAG responses. --- Need help customizing? Contact me for consulting and support or add me on Linkedin.
How to import this n8n workflow
- 1
Download the workflow JSON file after purchase.
- 2
Open n8n → click the menu → Import from File.
- 3
Select the downloaded JSON and import.
- 4
Set up credentials for each node that requires them.
- 5
Click Execute Workflow to test, then activate.
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